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Congestion Transition on Random Walks on Graphs.

Lorenzo Di Meco1,2, Mirko Degli Esposti1,2, Federico Bellisardi1,2

  • 1Department of Physics and Astronomy, University of Bologna, 40126 Bologna, Italy.

Entropy (Basel, Switzerland)
|August 29, 2024
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Summary
This summary is machine-generated.

Urban road congestion emerges from traffic load fluctuations. A simple transport model reveals how node states correlate, leading to empty or congested clusters, crucial for smart city mobility.

Keywords:
Markov processesentropic forcesmaster equation

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Area of Science:

  • Complex Systems
  • Urban Mobility
  • Network Science

Background:

  • Road network congestion impedes sustainable urban mobility.
  • Understanding congestion dynamics is vital for smart city development.
  • Existing models often simplify traffic flow dynamics.

Purpose of the Study:

  • To investigate the fundamental mechanisms of congestion formation in urban transport networks.
  • To analytically and numerically characterize traffic load distribution and node states.
  • To identify traffic load fluctuations as potential congestion precursors.

Main Methods:

  • A reductionist approach using a simple transport model on a graph.
  • Analytical characterization of traffic load distribution via a local maximum entropy principle (single-step approximation).
  • Numerical simulations of synchronous node dynamics to explore correlations.

Main Results:

  • Analytical solutions predict node congestion emergence analogous to percolation transitions.
  • Numerical simulations reveal entropic forces induce correlations and node state clustering.
  • Traffic load fluctuations are identified as key indicators of impending congestion.

Conclusions:

  • Congestion formation exhibits universal properties independent of specific network details.
  • Correlations in node states significantly impact congestion patterns in realistic scenarios.
  • Traffic load fluctuations offer a predictive signal for congestion in transport networks.